Asia

Thermal facial image analyses reveal quantitative hallmarks of aging and metabolic diseases

Zhengqing Yu ∙ Yong Zhou ∙ Kehang Mao ∙ … ∙ Hongxiao Liu ∙ Yi Wang ∙ Jing-Dong J. Han

Summary

Although human core body temperature is known to decrease with age, the age dependency of facial temperature and its potential to indicate aging rate or aging-related diseases remains uncertain. Here, we collected thermal facial images of 2,811 Han Chinese individuals 20–90 years old, developed the ThermoFace method to automatically process and analyze images, and then generated thermal age and disease prediction models. The ThermoFace deep learning model for thermal facial age has a mean absolute deviation of about 5 years in cross-validation and 5.18 years in an independent cohort. The difference between predicted and chronological age is highly associated with metabolic parameters, sleep time, and gene expression pathways like DNA repair, lipolysis, and ATPase in the blood transcriptome, and it is modifiable by exercise. Consistently, ThermoFace disease predictors forecast metabolic diseases like fatty liver with high accuracy (AUC > 0.80), with predicted disease probability correlated with metabolic parameters.

 

Read more…

Recent Posts

VivaTech 2025: Givaudan spotlights digital story-smelling and tech-driven fragrances via Premium Beauty News

Beatrice Wihlander 11 June 2025 Givaudan is showcasing its fragrance technologies, which are underscored by…

ISO 23675: A new validated standard for SPF determination by Zurko Research

After over 10 years of testing sunscreen products, at Zurko Research we are proud to…

This sticker reads emotions (even the ones you try to hide) via Popular Science

Mack Degueurin 22 April 2025 The wearable device analyzes the tiny changes in physical responses…